Systems and methods for magnetic resonance imaging reconstruction

ABSTRACT

A method for MRI reconstruction is provided. The method may include obtaining a plurality of sub-sampled images of a subject. The plurality of sub-sampled images may include a first sub-sampled image of the subject and one or more second sub-sampled images of the subject. The first sub-sampled image may be generated using a first MRI sequence and a first sub-sampling rate. Each of the one or more second sub-sampled images may be generated using a second MRI sequence and a second sub-sampling rate. The second sub-sampling rate may be smaller than the first sub-sampling rate. The method may include obtaining an image reconstruction model having been trained according to a machine learning technique. The method may further include generating a first full image of the subject corresponding to the first MRI sequence based on the first sub-sampled image, the one or more second sub-sampled images, and the image reconstruction model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201911419035.8, filed on Dec. 31, 2019, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to medical image processing, and moreparticularly, to systems and methods for image reconstruction in MRI.

BACKGROUND

Magnetic Resonance Imaging (MRI) is widely used in disease diagnosis andscientific research. However, MRI data is often acquired sequentially ink-space using an MRI system, and the traversal speed in k-space islimited by the MRI system and a Nyquist sampling theorem, which maycause a long sampling time of the MRI data in K-space and a slow imagingspeed. In addition, an MRI scan is usually performed in a relativelyclosed space and a patient may move involuntarily during the MRI scan,which may affect the imaging quality (e.g., cause motion artifacts in aresulting image). Recently, a sub-sampling technique has been used toaccelerate the imaging process in MRI. However, since the sub-samplingtechnique does not comply with the Nyquist sampling theorem, aliasingartifacts may be generated during an image reconstruction process.Therefore, it is desired to provide methods and systems for generatingMRI images with an improved image quality using the sub-samplingtechnique.

SUMMARY

An aspect of the present disclosure relates to a method for MRIreconstruction. The method may include obtaining a plurality ofsub-sampled images of a subject. The plurality of sub-sampled images mayinclude a first sub-sampled image of the subject and one or more secondsub-sampled images of the subject. The first sub-sampled image may begenerated using a first MRI sequence and a first sub-sampling rate. Eachof the one or more second sub-sampled images may be generated using asecond MRI sequence and a second sub-sampling rate. The secondsub-sampling rate may be smaller than the first sub-sampling rate. Themethod may include obtaining an image reconstruction model having beentrained according to a machine learning technique. The method mayfurther include generating a first full image of the subjectcorresponding to the first MRI sequence based on the first sub-sampledimage, the one or more second sub-sampled images, and the imagereconstruction model.

In some embodiments, the generating a first full image of the subjectcorresponding to the first MRI sequence may include obtaining one ormore reference full images of the subject each of which is generatedusing one of one or more third MRI sequences. The generating a firstfull image of the subject corresponding to the first MRI sequence mayfurther include generating the first full image of the subjectcorresponding to the first MRI sequence based on the first sub-sampledimage, the one or more second sub-sampled images, the imagereconstruction model, and the one or more reference full image.

In some embodiments, the image reconstruction model may include a firsttrained model and one or more second trained models. The first trainedmodel may be configured to process the first sub-sampled image. Each ofthe one or more second trained models may be configured to process oneof the one or more second sub-sampled images.

In some embodiments, the first trained model may correspond to the firstMRI sequence and the first sub-sampling rate. Each of the one or moresecond trained models may correspond to the second MRI sequence and thesecond sub-sampling rate of the second sub-sampled image processed bythe second trained model.

In some embodiments, in the image reconstruction model, the firsttrained model and the one or more second trained models may be arrangedaccording to their respective sub-sampling rates in ascending order andsequentially connected.

In some embodiments, an input of the first trained model may include anoutput of each of the one or more second trained models.

In some embodiments, the generating a first full image of the subjectcorresponding to the first MRI sequence may include, for each of the oneor more second sub-sampled images, generating a second full image byprocessing the second sub-sampled image using its corresponding secondtrained model. The generating a first full image of the subjectcorresponding to the first MRI sequence may further include generatingthe first full image by processing the one or more second full imagesand the first sub-sampled image using the first trained model.

In some embodiments, the one or more second sub-sampled images mayinclude a plurality of second sub-sampled images corresponding to aplurality of second sub-sampling rates. For each of the one or moresecond sub-sampled images, the generating a second full image byprocessing the second sub-sampled image using its corresponding secondtrained model may include generating a ranking result by ranking theplurality of second sub-sampled images according to their respectivesecond sub-sampling rates in ascending order, and sequentiallyprocessing the plurality of second sub-sampled images in the rankingresult to generate the plurality of second full images.

In some embodiments, the image reconstruction model may be generatedaccording to a model training process. The model training process mayinclude obtaining a plurality of training samples each of includes afirst sample sub-sampled image and one or more second sample sub-sampledimages of the sample subject. The first sample sub-sampled image may begenerated based on the first MRI sequence and the first sub-samplingrate. Each of the one or more second sample sub-sampled images may begenerated based on one of the one or more second MRI sequences and oneof the one or more second sub-sampling rates. The model training processmay include obtaining a preliminary model including a first model andone or more second models. The model training process may furtherinclude generating the one or more second trained models by training theone or more second models based on the one or more second samplesub-sampled images of each of the plurality of training samples. Themodel training process may further include generating the first trainedmodel by training the first model based on the first sample sub-sampledimage of each of the plurality of training samples and the one or moresecond trained models.

In some embodiments, each of the plurality of training samples mayfurther include one or more sample reference full images of thecorresponding sample subject. Each of the one or more sample referencefull images may be generated using one of the one or more third MRIsequences. The one or more second trained models and the first trainedmodel may be generated further based on the one or more sample referencefull images of each of the plurality of training samples.

In some embodiments, the generating the first trained model by trainingthe first model may include for each of the plurality of trainingsamples, obtaining one or more predicted images corresponding to the oneor more second MRI sequences based on the one or more second trainedmodels and the one or more second sample sub-sampled images of thetraining sample. The generating the first trained model by training thefirst model may further include generating the first trained model bytraining the first model based on the first sub-sampled image and theone or more predicted images of each of the plurality of trainingsamples.

Another aspect of the present disclosure relates to a system for MRIreconstruction. The system may include at least one storage deviceincluding a set of instructions and at least one processor incommunication with the at least one storage device. When executing theset of instructions, the at least one processor may be directed toperform operations. The operations may include obtaining a plurality ofsub-sampled images of a subject. The plurality of sub-sampled images mayinclude a first sub-sampled image of the subject and one or more secondsub-sampled images of the subject. The first sub-sampled image may begenerated using a first MRI sequence and a first sub-sampling rate. Eachof the one or more second sub-sampled images may be generated using asecond MRI sequence and a second sub-sampling rate. The secondsub-sampling rate may be smaller than the first sub-sampling rate. Theoperations may include obtaining an image reconstruction model havingbeen trained according to a machine learning technique. The operationsmay further include generating a first full image of the subjectcorresponding to the first MRI sequence based on the first sub-sampledimage, the one or more second sub-sampled images, and the imagereconstruction model.

A further aspect of the present disclosure relates to a non-transitorycomputer readable medium including executable instructions for MRIreconstruction. When the executable instructions are executed by atleast one processor, the executable instructions may direct the at leastone processor to perform a method. The method may include obtaining aplurality of sub-sampled images of a subject. The plurality ofsub-sampled images may include a first sub-sampled image of the subjectand one or more second sub-sampled images of the subject. The firstsub-sampled image may be generated using a first MRI sequence and afirst sub-sampling rate. Each of the one or more second sub-sampledimages may be generated using a second MRI sequence and a secondsub-sampling rate. The second sub-sampling rate may be smaller than thefirst sub-sampling rate. The method may include obtaining an imagereconstruction model having been trained according to a machine learningtechnique. The method may further include generating a first full imageof the subject corresponding to the first MRI sequence based on thefirst sub-sampled image, the one or more second sub-sampled images, andthe image reconstruction model.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary MRI systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generating afirst full image of a subject according to some embodiment of thepresent disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generating afirst full image of a subject according to some embodiments of thepresent disclosure;

FIG. 7 is a schematic diagram illustrating an exemplary process forgenerating a first full image of a subject according to some embodimentsof the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generatingan image reconstruction model according to some embodiments of thepresent disclosure; and.

FIG. 9 is a schematic diagram illustrating an exemplary process forgenerating an image reconstruction model according to some embodimentsof the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “device,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assembly ofdifferent levels in ascending order. However, the terms may be displacedby another expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or themselves,and/or may be invoked in response to detected events or interrupts.Software modules/units/blocks configured for execution on computingdevices (e.g., processor 210 as illustrated in FIG. 2) may be providedon a computer-readable medium, such as a compact disc, a digital videodisc, a flash drive, a magnetic disc, or any other tangible medium, oras a digital download (and can be originally stored in a compressed orinstallable format that needs installation, decompression, or decryptionprior to execution). Such software code may be stored, partially orfully, on a storage device of the executing computing device, forexecution by the computing device. Software instructions may be embeddedin firmware, such as an EPROM. It will be further appreciated thathardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description mayapply to a system, a device, or a portion thereof.

It will be understood that when a unit, device, module, or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, device, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, device, module, orblock, or an intervening unit, device, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “pixel” and “voxel” in the presentdisclosure are used interchangeably to refer to an element in an image.The term “image” in the present disclosure is used to refer to images ofvarious forms, including a 2-dimensional image, a 3-dimensional image, a4-dimensional image, etc.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Moreover, while the systems and methods disclosed in the presentdisclosure are described primarily regarding image reconstruction in anMRI system. It should be understood that this is only for illustrationpurposes. The systems and methods of the present disclosure may beapplied to reconstruct image data acquired in different scenarios and/orfor different purposes (e.g., safety monitoring, filming, orphotography) and/or by different image devices (e.g., a computedtomography (CT) scanner, a positron emission tomography (PET) scanner).

For example, the systems and methods of the present disclosure may beapplied to any other kind of medical imaging system. In someembodiments, the imaging system may include a single modality imagingsystem and/or a multi-modality imaging system. The single modalityimaging system may include, for example, the MRI system. Themulti-modality imaging system may include, for example, a computedtomography-magnetic resonance imaging (MRI-CT) system, a positronemission tomography-magnetic resonance imaging (PET-MRI) system, asingle photon emission computed tomography-magnetic resonance imaging(SPECT-MRI) system, a digital subtraction angiography-magnetic resonanceimaging (DSA-MRI) system, etc.

MRI systems are widely used in medical diagnosis and/or treatment byexploiting a powerful magnetic field and radio frequency (RF)techniques. Normally, full k-space data of a subject may need to becollected for reconstructing a full MRI image of the subject. In orderto accelerate the data acquisition and reduce the scan time, a fractionof the full k-space data (i.e., a set of sub-sampled k-space data) maybe acquired by sub-sampling technique with, for example, a reducednumber of k-space sampling steps, a reduced number of samples per line,a reduced number of lines per blade, a reduced number of blades peracquisition, or the like, or any combination thereof. The sub-samplingtechnique may improve the efficiency of MRI scanning. However, it isdifficult to reconstruct an accurate full image from a set ofsub-sampled k-space data acquired in a sub-sampling scan because of theinformation loss during the sub-sampling scan, especially when asub-sampling rate of the sub-sampling scan is high (e.g., higher than athreshold). Therefore, it is desired to provide systems and methods forreconstructing a full image with an improved image quality for asub-sampling scan.

An aspect of the present disclosure provides systems and methods for MRIreconstruction. The systems may obtain a plurality of sub-sampled imagesof a subject. The plurality of sub-sampled images may include a firstsub-sampled image of the subject and one or more second sub-sampledimages of the subject. The first sub-sampled image may be generatedusing a first MRI sequence and a first sub-sampling rate. Each of theone or more second sub-sampled images may be generated using a secondMRI sequence and a second sub-sampling rate. The second sub-samplingrate may be smaller than the first sub-sampling rate. The systems mayalso obtain an image reconstruction model having been trained accordingto a machine learning technique. Further, the systems may generate afirst full image of the subject corresponding to the first MRI sequencebased on the first sub-sampled image, the one or more second sub-sampledimages using the image reconstruction model. Optionally, the systems mayalso obtain one or more reference full images of the subject each ofwhich is generated using one of one or more third MRI sequences, and usethe reference full image(s) in the generation of the first full image.

According to some embodiments of the present disclosure, the firstsub-sampled image corresponding to the first sub-sampling rate may bereconstructed into the first full image based on one or more secondsub-sampled images corresponding to the second sub-sampling rate(s)lower than the first sub-sampling rate and the reference full image(s)(optionally) of the subject using an image reconstruction model. Thesecond sub-sampled image(s) may be acquired using lower sub-samplingrate(s) than the first MRI sequence, and have more image features andphysiological information than the first sub-sampled image. Therefore,the second sub-sampled image(s) and the reference full image may providereference information for assisting the image reconstruction model togenerate the first full image. The reference information provided by thereference full image and the second sub-sampled image(s) may facilitatethe information recovery in the reconstruction of the first full image,and improve the image quality and the reconstruction speed of the firstfull image. In this way, the total scan time of the subject may bereduced by adopting the sub-sampling imaging technique to acquire thefirst and second sub-sampled images instead of directly acquiring thefirst and second full images. Moreover, the reconstruction efficiency ofthe first full image may be improved because the application of theimage reconstruction model may obviate the need of performing acalibration process between the first and second sub-sampled images.

In some embodiments, the image reconstruction model may include a firsttrained model and one or more second trained models. The first trainedmodel may be configured to process the first sub-sampled image. Each ofthe second trained model(s) may be configured to process one of thesecond sub-sampled image(s). The first trained model and the one or moresecond trained models may be arranged according to their respectivesub-sampling rates in ascending order and sequentially connected. Duringthe application of the image reconstruction model, an input of the firsttrained model may include an output of each of the one or more secondtrained models. The output of each of the second trained model(s) mayfacilitate the first trained model to generate a first full image withan improved accuracy (e.g., having less artifacts and/or noises, andhaving more image features).

FIG. 1 is a schematic diagram illustrating an exemplary MRI system 100according to some embodiments of the present disclosure. As shown inFIG. 1, the MRI system 100 may include an MRI scanner 110, a processingdevice 120, a storage device 130, one or more terminals 140, and anetwork 150. In some embodiments, the MRI scanner 110, the processingdevice 120, the storage device 130, and/or the terminal(s) 140 may beconnected to and/or communicate with each other via a wirelessconnection, a wired connection, or a combination thereof. Theconnections between the components in the MRI system 100 may bevariable. For example, the MRI scanner 110 may be connected to theprocessing device 120 through the network 150. As another example, theMRI scanner 110 may be connected to the processing device 120 directly.

The MRI scanner 110 may be configured to scan a subject (or a part ofthe subject) to acquire image data, such as MRI signals associated withthe subject. For example, the MRI scanner 110 may detect a plurality ofMRI signals by applying an MRI sequence on the subject. In someembodiments, the MRI scanner 110 may include, for example, a magneticbody, a gradient coil, an RF coil, etc. In some embodiments, the MRIscanner 110 may be a permanent magnet MRI scanner, a superconductingelectromagnet MRI scanner, or a resistive electromagnet MRI scanner,etc., according to the types of the magnetic body. In some embodiments,the MRI scanner 110 may be a high-field MRI scanner, a mid-field MRIscanner, and a low-field MRI scanner, etc., according to the intensityof the magnetic field.

In the present disclosure, “subject” and “object” are usedinterchangeably. The subject may be biological or non-biological. Forexample, the subject may include a patient, a man-made subject, etc. Asanother example, the subject may include a specific portion, organ,tissue, and/or a physical point of the patient. For example, the subjectmay include head, brain, neck, body, shoulder, arm, thorax, cardiac,stomach, blood vessel, soft tissue, knee, feet, or the like, or acombination thereof.

In some embodiments, the processing device 120 may be a single server ora server group. The server group may be centralized or distributed. Theprocessing device 120 may process data and/or information obtained fromthe MRI scanner 110, the storage device 130, and/or the terminal(s) 140.For example, the processing device 120 may obtain a plurality ofsub-sampled images of a subject and an image reconstruction model havingbeen trained according to a machine learning technique, and generate afull image of the subject based on the plurality of sub-sampled imagesand the image reconstruction model. As another example, the processingdevice 120 may obtain a plurality of training samples and a preliminarymodel, and generate the image reconstruction model by training thepreliminary model based on the plurality of training samples.

In some embodiments, a trained model (e.g., the image reconstructionmodel) may be generated by a processing device, while the application ofthe trained model may be performed on a different processing device. Insome embodiments, the trained model may be generated by a processingdevice of a system different from the MRI system 100 or a serverdifferent from the processing device 120 on which the application of thetrained model is performed. For instance, the image reconstruction modelmay be generated by a first system of a vendor who provides and/ormaintains such an image reconstruction model, while the generation of afull image based on the provided image reconstruction model may beperformed on a second system of a client of the vendor. In someembodiments, the application of the trained model may be performedonline in response to a request for generating a full image of asubject. In some embodiments, the trained model may be determined orgenerated offline.

In some embodiments, the trained model may be determined and/or updated(or maintained) by, e.g., the manufacturer of the MRI scanner 110 or avendor. For instance, the manufacturer or the vendor may load the imagereconstruction model into the MRI system 100 or a portion thereof (e.g.,the processing device 120) before or during the installation of the MRIscanner 110 and/or the processing device 120, and maintain or update theimage reconstruction model from time to time (periodically or not). Themaintenance or update may be achieved by installing a program stored ona storage device (e.g., a compact disc, a USB drive, etc.) or retrievedfrom an external source (e.g., a server maintained by the manufactureror vendor) via the network 150. The program may include a new model(e.g., a newly trained model) or a portion of a model that substitutesor supplement a corresponding portion of the model.

In some embodiments, the processing device 120 may be local or remotefrom the MRI system 100. For example, the processing device 120 mayaccess information and/or data from the MRI scanner 110, the storagedevice 130, and/or the terminal(s) 140 via the network 150. As anotherexample, the processing device 120 may be directly connected to the MRIscanner 110, the terminal(s) 140, and/or the storage device 130 toaccess information and/or data. In some embodiments, the processingdevice 120 may be implemented on a cloud platform. For example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or a combination thereof. In some embodiments,the processing device 120 may be implemented by a computing device 200having one or more components as described in connection with FIG. 2.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the MRI scanner 110, the processing device 120, and/or theterminal(s) 140. In some embodiments, the storage device 130 may storedata and/or instructions that the processing device 120 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage device 130 may include a mass storagedevice, a removable storage device, a volatile read-and-write memory, aread-only memory (ROM), or the like, or a combination thereof. Exemplarymass storage devices may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage devices may includea flash drive, a floppy disk, an optical disk, a memory card, a zipdisk, a magnetic tape, etc. Exemplary volatile read-and-write memory mayinclude a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), a zero-capacitorRAM (Z-RAM), etc. Exemplary ROM may include a mask ROM (MROM), aprogrammable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), a digital versatile disk ROM, etc. In some embodiments, thestorage device 130 may be implemented on a cloud platform as describedelsewhere in the disclosure.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in the MRIsystem 100 (e.g., the MRI scanner 110, the processing device 120, and/orthe terminal(s) 140). One or more components of the MRI system 100 mayaccess the data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be part ofthe processing device 120 or the terminal(s) 140.

The terminal(s) 140 may be configured to enable a user interactionbetween a user and the MRI system 100. For example, the terminal(s) 140may receive an instruction to cause the MRI scanner 110 to scan thesubject from the user. As another example, the terminal(s) 140 mayreceive a processing result (e.g., a reconstructed MRI image of asubject) from the processing device 120 and display the processingresult to the user. In some embodiments, the terminal(s) 140 may beconnected to and/or communicate with the MRI scanner 110, the processingdevice 120, and/or the storage device 130. In some embodiments, theterminal(s) 140 may include a mobile device 141, a tablet computer 142,a laptop computer 143, or the like, or a combination thereof. Forexample, the mobile device 141 may include a mobile phone, a personaldigital assistant (PDA), a gaming device, a navigation device, a pointof sale (POS) device, a laptop, a tablet computer, a desktop, or thelike, or a combination thereof.

In some embodiments, the terminal(s) 140 may include an input device, anoutput device, etc. The input device may include alphanumeric and otherkeys that may be input via a keyboard, a touch screen (for example, withhaptics or tactile feedback), a speech input, an eye tracking input, abrain monitoring system, or any other comparable input mechanism. Theinput information received through the input device may be transmittedto the processing device 120 via, for example, a bus, for furtherprocessing. Other types of the input device may include a cursor controldevice, such as a mouse, a trackball, or cursor direction keys, etc. Theoutput device may include a display, a speaker, a printer, or the like,or a combination thereof. In some embodiments, the terminal(s) 140 maybe part of the processing device 120 or the MRI scanner 110.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the MRI system 100. In someembodiments, one or more components of the MRI system 100 (e.g., the MRIscanner 110, the processing device 120, the storage device 130, theterminal(s) 140, etc.) may communicate information and/or data with oneor more other components of the MRI system 100 via the network 150. Forexample, the processing device 120 may obtain image data (e.g., MRIsignals) from the MRI scanner 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150.

The network 150 may include a public network (e.g., the Internet), aprivate network (e.g., a local area network (LAN), a wide area network(WAN)), etc.), a wired network (e.g., an Ethernet network), a wirelessnetwork (e.g., an 802.11 network, a Wi-Fi network, etc.), a cellularnetwork (e.g., a Long Term Evolution (LTE) network), a frame relaynetwork, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, or thelike, or a combination thereof. For example, the network 150 may includea cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or a combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the MRI system 100 may beconnected to the network 150 to exchange data and/or information.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. In some embodiments, the MRIsystem 100 may include one or more additional components and/or one ormore components described above may be omitted. Additionally oralternatively, two or more components of the MRI system 100 may beintegrated into a single component. For example, the processing device120 may be integrated into the MRI scanner 110. As another example, acomponent of the MRI system 100 may be replaced by another componentthat can implement the functions of the component. In some embodiments,the storage device 130 may be a data storage including cloud-computingplatforms, such as a public cloud, a private cloud, a community, andhybrid cloud, etc. However, those variations and modifications do notdepart the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device 200 according to someembodiments of the present disclosure. The computing device 200 may beused to implement any component of the MRI system 100 as describedherein. For example, the processing device 120 and/or a terminal 140 maybe implemented on the computing device 200, respectively, via itshardware, software program, firmware, or a combination thereof. Althoughonly one such computing device is shown, for convenience, the computerfunctions relating to the MRI system 100 as described herein may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load. As illustrated in FIG. 2, thecomputing device 200 may include a processor 210, a storage device 220,an input/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataof a subject obtained from the MRI scanner 110, the storage device 130,terminal(s) 140, and/or any other component of the MRI system 100. Asanother example, the processor 210 may generate a full MRI image of thesubject based on the (processed) image data of the subject.

In some embodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combination thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. The operations and/or method steps that are performed by oneprocessor as described in the present disclosure may also be jointly orseparately performed by the multiple processors. For example, if in thepresent disclosure the processor of the computing device 200 executesboth operation A and operation B, it should be understood that operationA and operation B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes operation A and a second processor executesoperation B, or the first and second processors jointly executeoperations A and B).

The storage device 220 may store data/information obtained from the MRIscanner 110, the storage device 130, the terminal(s) 140, and/or anyother component of the MRI system 100. In some embodiments, the storagedevice 220 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or a combination thereof. In some embodiments, the storagedevice 220 may store one or more programs and/or instructions to performexemplary methods described in the present disclosure. For example, thestorage device 220 may store a program for the processing device 120 toexecute to generate a trained model (e.g., an image reconstructionmodel). As another example, the storage device 220 may store a programfor the processing device 120 to execute to apply the trained model(e.g., the image reconstruction model) to generate a full image of asubject.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. The input device may includealphanumeric and other keys that may be input via a keyboard, a touchscreen (for example, with haptics or tactile feedback), a speech input,an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be transmitted to another component (e.g., theprocessing device 120) via, for example, a bus, for further processing.Other types of the input device may include a cursor control device,such as a mouse, a trackball, or cursor direction keys, etc. The outputdevice may include a display (e.g., a liquid crystal display (LCD), alight-emitting diode (LED)-based display, a flat panel display, a curvedscreen, a television device, a cathode ray tube (CRT), a touch screen),a speaker, a printer, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and oneor more components of the MRI system 100 (e.g., the MRI scanner 110, thestorage device 130, and/or the terminal(s) 140). The connection may be awired connection, a wireless connection, any other communicationconnection that can enable data transmission and/or reception, and/or acombination of these connections. The wired connection may include, forexample, an electrical cable, an optical cable, a telephone wire, or thelike, or a combination thereof. The wireless connection may include, forexample, a Bluetooth™ link, a Wi-Fi™ link, a WiMax™ link, a WLAN link, aZigBee link, a mobile network link (e.g., 3G, 4G, 5G, etc.), or thelike, or a combination thereof. In some embodiments, the communicationport 240 may be and/or include a standardized communication port, suchas RS232, RS485, etc. In some embodiments, the communication port 240may be a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device 300 according to some embodimentsof the present disclosure. In some embodiments, one or more componentsof the MRI system 100 may be implemented on one or more components ofthe mobile device 300. Merely by way of example, a terminal 140 may beimplemented on one or more components of the mobile device 300.

As illustrated in FIG. 3, the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS™, Android™,Windows Phone™, etc.) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating tothe MRI system 100. User interactions with the information stream may beachieved via the I/O 350 and provided to the processing device 120and/or other components of the MRI system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure. Theprocessing devices 120A and 120B may be exemplary embodiments of theprocessing device 120 as described in connection with FIG. 1. In someembodiments, the processing device 120A may be configured to generate animage reconstruction model. The processing device 120B may be configuredto apply the image reconstruction model in generating a full image of asubject. In some embodiments, the processing devices 120A and 120B maybe respectively implemented on a processing unit (e.g., a processor 210illustrated in FIG. 2 or a CPU 340 as illustrated in FIG. 3). Merely byway of example, the processing devices 120A may be implemented on thecomputing device 200, and the processing device 120B may be implementedon a CPU 340 of a terminal device. Alternatively, the processing devices120A and 120B may be implemented on a same computing device 200 or asame CPU 340. For example, the processing devices 120A and 120B may beimplemented on a same computing device 200.

As shown in FIG. 4A, the processing device 120A may include an obtainingmodule 410 and a model generation module 420.

The obtaining module 410 may be configured to obtain information forgenerating the image reconstruction model. For example, the obtainingmodule 410 may be configured to obtain a plurality of training sampleseach of which includes a first sample sub-sampled image and one or moresecond sample sub-sampled images of a sample subject. In someembodiments, each of the plurality of training samples may furtherinclude one or more sample reference full images of the correspondingsample subject. More descriptions regarding the obtaining of theplurality of training samples may be found elsewhere in the presentdisclosure. See, e.g., operation 810 in FIG. 8 and relevant descriptionsthereof. As another example, the obtaining module 410 may be configuredto obtain a preliminary model including a first model and one or moresecond models. The preliminary model refers to an algorithm or a model(e.g., a machine learning model) to be trained as the imagereconstruction model. The first model refers to an algorithm or a modelto be trained as the first trained model as described in connection withFIG. 5. The second model(s) refers to an algorithm or a model to betrained as the second trained model(s) as described in connection withFIG. 5. More descriptions regarding the obtaining of the preliminarymodel may be found elsewhere in the present disclosure. See, e.g.,operation 820 in FIG. 8 and relevant descriptions thereof.

The model generation module 420 may be configured to generate the one ormore second trained models by training the one or more second modelsbased on the one or more second sample sub-sampled images of each of theplurality of training samples. More descriptions regarding thegeneration of the one or more second trained models may be foundelsewhere in the present disclosure. See, e.g., operation 830 in FIG. 8and relevant descriptions thereof. The model generation module 420 maybe further configured to generate the first trained model by trainingthe first model based on the first sample sub-sampled image of each ofthe plurality of first training samples and the one or more secondtrained models. More descriptions regarding the generation of the firsttrained model may be found elsewhere in the present disclosure. See,e.g., operation 840 in FIG. 8 and relevant descriptions thereof.

As shown in FIG. 4B, the processing device 120B may include an obtainingmodule 430 and an image generation module 440.

The obtaining module 430 may be configured to obtain information forgenerating a full image of a subject. For example, the obtaining module430 may be configured to obtain a plurality of sub-sampled images of thesubject. The plurality of sub-sampled images of the subject obtained in510 may include a first sub-sampled image of the subject and one or moresecond sub-sampled images of the subject. The first sub-sampled imagemay be generated using a first MRI sequence and a first sub-samplingrate, and the one or more second sub-sampled images may be generatedusing one or more second MRI sequences and one or more secondsub-sampling rates. Each of the second sub-sampled image(s) may begenerated using one of the second MRI sequence(s) and one of the secondsub-sampling rate(s). In some embodiments, the obtaining module 430 maybe configured to obtain one or more reference full images of thesubject. More descriptions regarding the obtaining of the plurality ofsub-sampled images of the subject may be found elsewhere in the presentdisclosure. See, e.g., operation 510 in FIG. 5 and relevant descriptionsthereof.

As another example, the obtaining module 430 may be configured to obtainthe image reconstruction model. More descriptions regarding theobtaining of the image reconstruction model may be found elsewhere inthe present disclosure. See, e.g., operation 520 in FIG. 5 and relevantdescriptions thereof.

The image generation module 440 may be configured to generate a firstfull image of the subject corresponding to the first MRI sequence. Forexample, for each of the second sub-sampled image(s), the imagegeneration module 440 may be configured to generate a second full imageby processing the second sub-sampled image using its correspondingsecond trained model. More descriptions regarding the generation of thesecond full image of the subject may be found elsewhere in the presentdisclosure. See, e.g., operation 610 in FIG. 6 and relevant descriptionsthereof. The image generation module 440 may be further configured togenerate the first full image by processing the second full image(s) andthe first sub-sampled image using the first trained model. Moredescriptions regarding the generation of the first full image of thesubject may be found elsewhere in the present disclosure. See, e.g.,operation 620 in FIG. 6 and relevant descriptions thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, the processing device 120A and/or the processing device1206 may share two or more of the modules, and any one of the modulesmay be divided into two or more units. For instance, the processingdevices 120A and 120B may share a same obtaining module; that is, theobtaining module 410 and the obtaining module 430 are a same module. Insome embodiments, the processing device 120A and/or the processingdevice 120B may include one or more additional modules, such as astorage module (not shown) for storing data. In some embodiments, theprocessing device 120A and the processing device 120B may be integratedinto one processing device 120.

FIG. 5 is a flowchart illustrating an exemplary process for generating afirst full image of a subject according to some embodiment of thepresent disclosure. In some embodiments, process 500 may be executed bythe MRI system 100. For example, the process 500 may be implemented as aset of instructions (e.g., an application) stored in a storage device(e.g., the storage device 130, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 120B (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4B) mayexecute the set of instructions and may accordingly be directed toperform the process 500.

In 510, the processing device 120B (e.g., the obtaining module 430) mayobtain a plurality of sub-sampled images of the subject.

As used herein, the subject may include a biological subject and/or anon-biological subject, such as a patient or a specific portion (e.g.,an organ or a tissue) of the patient, an animal, or the like. Merely byway of example, the subject may include at least a portion of an organ(e.g., the brain, the lungs, the liver) or a tissue of a patient.

A sub-sampled image may be a 2D image (e.g., a slice image) or a 3Dimage. In some embodiments, the sub-sampled image of the subject may beacquired by performing an MRI scan on the subject using a sub-samplingtechnique (or referred to as a sub-sampling scan). Normally, fullk-space data MRI may need to be collected for reconstructing a full MRIimage of the subject. In order to accelerate the data acquisition andreduce the scan time, a fraction of the full k-space data (i.e., a setof sub-sampled k-space data) may be acquired by the sub-samplingtechnique with, for example, a reduced number of k-space sample steps, areduced number of samples per line, a reduced number of lines per blade,a reduced number of blades per acquisition, or the like, or anycombination thereof. It can be understood that image features andphysiological information included in a sub-sampled image acquired byperforming a sub-sampling scan may be less than that included in a fullimage acquired by performing a full-sampling scan. As used herein, asub-sampling scan refers to an MRI scan performed using the sub-samplingtechnique, and a full-sampling scan refers to an MRI scan performedwithout using the sub-sampling technique.

In some embodiments, the sub-sampling scan may be performed by an MRIscanner (e.g., the MRI scanner 110) based on an MRI sequence and asub-sampling rate. The MRI sequence may be defined by one or more scanparameters, such as the type of the MRI pulse sequence, a time forapplying the MRI pulse sequence, a duration of the MRI pulse sequence, aflip angle of an RF pulse in the MRI pulse sequence, a count (or number)of RF pulses in the MRI pulse sequence, a unit repetition time (TR), arepetition count, an inversion time (TI), a gradient, a signalacquisition time, or the like, or any combination thereof. The type ofthe MRI sequence may include a spin-echo (SE) sequence, a fast spin-echo(FSE) sequence, a gradient-echo (GRE) sequence, a fast gradient-echo(FGRE) sequence, a steady-state free precession (SSFP) sequence, aninversion recovery (IR) sequence, an echo-planar imaging (EPI) sequence,or the like, or a combination thereof. The IR sequence may include ashort time inversion recovery (STIR) sequence and a fluid-attenuatedinversion recovery (FLAIR) sequence. Different MRI sequences havedifferent values of the scan parameters. For example, by settingdifferent TEs and repetition times, a spin-echo sequence may beclassified into different types, such as a T1W1 sequence, a T2W1sequence, a PDW1 sequence, or the like. A T1-weighted image may beobtained using the T1W1 sequence, a T2-weighted image may be obtainedusing the T2W1 sequence, and a proton-weighted image may be obtainedusing the PDW1 sequence.

The sub-sampling rate may be measured by, for example, a ratio of thedata volume of full K-space data to that of sub-sampled K-space dataacquired in the sub-sampling scan. For example, assuming that the valueof the sub-sampling rate corresponding to a full-sampling scan is 1, thevalue of the sub-sampling rate corresponding to the sub-sampling scanmay be greater than 1, such as 2, 2.25, 3.5, or the like. A largersub-sampling rate may result in a set of sub-sampled k-space data havingfewer image features and less physiological information, and a resultingsub-sampled image having a lower image quality (e.g., more artifacts).

In some embodiments, the processing device 1206 may generate thesub-sampled image by mapping the corresponding set of sub-sampledK-space data (or the original sub-sampled MRI signals collected in thesub-sampling scan) into the image domain using at least one operation,such as an inverse Fourier transformation. Alternatively, thesub-sampled image may be previously generated by a computing device(e.g., the processing device 1206) and stored in a storage device (e.g.,the storage device 130, the storage device 220, the storage 390, or anexternal storage device). The processing device 1206 may retrieve thesub-sampled image from the storage device.

In some embodiments, the plurality of sub-sampled images of the subjectobtained in 510 may include a first sub-sampled image of the subject andone or more second sub-sampled images of the subject. The firstsub-sampled image may be generated using a first MRI sequence and afirst sub-sampling rate, and the one or more second sub-sampled imagesmay be generated using one or more second MRI sequences and one or moresecond sub-sampling rates. Each of the second sub-sampled image(s) maybe generated using one of the second MRI sequence(s) and one of thesecond sub-sampling rate(s).

In some embodiments, each of the second sub-sampling rate(s) may besmaller than the first sub-sampling rate. For example, the one or moresecond sub-sampling rates may be 1.5, 2, and 2.25, and the value of thefirst sub-sampling rate may be 3.5. As described above, a largersub-sampling rate may result in a set of sub-sampled k-space data havingfewer image features and less physiological information, and asub-sampled image having a lower image quality. In such cases, eachsecond sub-sampled image may have better image quality and morephysiological information than the first sub-sampled image.

In some embodiments, the second sub-sampling rates corresponding todifferent second sub-sampled images may be the same or different. Thefirst MRI sequence and a second MRI sequence may be of the same type ordifferent types. The second MRI sequences corresponding to differentsecond sub-sampled images may be of the same type or different types.For example, the first MRI sequence and the second MRI sequence(s) mayboth be a T1W1 MRI sequence. As another example, the first MRI sequencemay be a T1W1 MRI sequence, the second MRI sequence(s) may be a T2W1 MRIsequence and a PDW1 MRI sequence. In some embodiments, if the second MRIsequences corresponding to different second sub-sampled images are ofthe same type, the second sub-sampling rates corresponding to thedifferent second sub-sampled images may be different. For example, iftwo second sub-sampled images are both generated using a T1W1 sequence,the two second sub-sampled images may be acquired using different secondsub-sampling rates.

In some embodiments, the processing device 120B (e.g., the obtainingmodule 430) may further obtain one or more reference full images of thesubject. Each of the reference full image(s) may be generated using oneof one or more third MRI sequences. A third MRI sequence may be of asame type as or a different type from the first MRI sequence or a secondMRI sequence. For example, the third MRI sequence and the first MRIsequence may both be a SE MRI sequence. As another example, the thirdMRI sequence may be a T1W1 MRI sequence, the first MRI sequence may be aGRE MRI sequence, and the second MRI sequence(s) may be a PDW1 MRIsequence.

In some embodiments, the first MRI sequence, the second MRI sequence(s),and the third MRI sequence(s) may be of different types. Additionally oralternatively, the second sub-sampling rates of different second MRIsequences may be different. Merely by way of example, a third MRIsequence is a SE MRI sequence, the first MRI sequence is a GRE MRIsequence corresponding to a first sub-sampling rate of 3, and a secondMRI sequence is a FLAIR MRI sequence corresponding to a secondsub-sampling rate of 2. As another example, a third MRI sequence is aT2W1 MRI sequence, the first MRI sequence is a GRE MRI sequencecorresponding to a first sub-sampling rate of 3, a second MRI sequenceis a T1W1 MRI sequence corresponding to a second sub-sampling rate of 2,and another second MRI sequence is a PDW1 MRI sequence corresponding toa second sub-sampling rate of 2.25.

In some embodiments, the count of the third MRI sequence(s) may bedenoted as n, and the total count of the first and second MRI sequencesmay be denoted as m. Because a sub-sampling scan normally has a shortersampling time and a higher imaging speed than a full-sampling scan, thetotal count of the first and second MRI sequences may be greater thanthe count of the third MRI sequence(s), that is, m>n. Merely by way ofexample, n is 1 and m is 3. This may reduce the scan time of the subjectand improve the scanning efficiency.

In some embodiments, the processing device 120B may obtain the firstsub-sampled image, the second sub-sampled image(s), and the referencefull image(s) from an MRI scanner in real-time or a storage device(e.g., the storage device 130, the storage device 220, and/or thestorage 390). Merely by way of example, a combined MRI scan may beperformed on the subject by the MRI scanner based on at least one MRIsequence and at least one sub-sampling rate. The combined MRI scan mayinclude a full-sampling scan performed according to a T2WI MRI sequence.Full k-space data of the subject may be generated by filling MRI signalscollected in the full-sampling scan into K-space (e.g., by performingphase encoding and/or frequency encoding), and a reference full image ofthe subject may be generated based on the full k-space data. Thecombined MRI scan may further include a sub-sampling scan S1 performedaccording to a T1W1 MRI sequence and a sub-sampling rate of 2, asub-sampling scan S2 performed according to a PDW1 MRI sequence and asub-sampling rate of 2.25, and a sub-sampling scan S3 performedaccording to a GRE MRI sequence and a sub-sampling rate of 3. MRIsignals collected in each of the sub-sampling scans S1, S2, and S3 maybe filled into k-space to generate a corresponding set of sub-sampledk-space data of the subject, which may be used to generate acorresponding sub-sampled image of the subject. The reference full imageand three sub-sampled images may be sent to a computing device (e.g.,the processing device 1206) for further processing, or a storage device(e.g., the storage device 130, the storage device 220, and/or thestorage 390) for storing. Among the three sub-sampled images, thesub-sampled image acquired by the sub-sampling scan S3 with the highestsub-sampling rate may be regarded as a first sub-sampled image, and theother two sub-sampled images may be regarded as two second sub-sampledimages.

In some embodiments, a sub-sampling scan may be performed by an MRIscanner including a plurality of coil units. A sub-sampled image (e.g.,the first sub-sampled image, a second sub-sampled image) acquired by thesub-sampling scan may be generated based on data acquired by one or moreof the plurality of coil units. For example, a coil image of each coilunit may be generated based on MRI data collected by the coil unit, andthe sub-sampled image may be generated by combining the coil images ofthe coil units. As another example, the sub-sampled image may begenerated based on MRI data collected by a single coil unit of the coilunits.

In 520, the processing device 120B (e.g., the obtaining module 430) mayobtain an image reconstruction model.

An image reconstruction model refers to a model (e.g., a machinelearning model) or an algorithm for MRI image reconstruction. Forexample, the image reconstruction model may be configured to reconstructa sub-sampled image (or sub-sampled k-space data) into a predicted fullimage. In some embodiments, the image reconstruction model may be of amachine learning model, such as a neural network model. For example, theimage reconstruction model may include a Fully Convolutional Network(FCN) model, a V-net model, a U-net model, an Alex network (AlexNet)model, a ResUNet model, a VB-net model, a Visual Geometry Group network(VGGNet) model, or the like, or any combination thereof.

In some embodiments, the image reconstruction model may be obtained fromone or more components of the MRI system 100 or an external source via anetwork (e.g., the network 150). For example, the image reconstructionmodel may be previously trained by a computing device (e.g., theprocessing device 120A), and stored in a storage device (e.g., thestorage device 130, the storage device 220, and/or the storage 390) ofthe MRI system 100. The processing device 120B may access the storagedevice and retrieve the image reconstruction model.

In some embodiments, the image reconstruction model may have beentrained according to a machine learning technique. Exemplary machinelearning techniques may include an artificial neural network algorithm,a deep learning algorithm, a decision tree algorithm, an associationrule algorithm, an inductive logic programming algorithm, a supportvector machine algorithm, a clustering algorithm, a Bayesian networkalgorithm, a reinforcement learning algorithm, a representation learningalgorithm, a similarity and metric learning algorithm, a sparsedictionary learning algorithm, a genetic algorithm, a rule-based machinelearning algorithm, or the like, or any combination thereof. The machinelearning technique used to train the Image reconstruction model may be asupervised learning algorithm, a semi-supervised learning algorithm, anunsupervised learning algorithm, or the like. In some embodiments, theimage reconstruction model may be trained by a computing device (e.g.,the processing device 120A) by performing a process (e.g., process 800)for training an image reconstruction model disclosed herein. Moredescriptions regarding the training of the image reconstruction modelmay be found elsewhere in the present disclosure. See, e.g., FIG. 8 andrelevant descriptions thereof.

In some embodiments, the image reconstruction model may include a firsttrained model and one or more second trained models. The first trainedmodel may be configured to process the first sub-sampled image. Each ofthe second trained model(s) may be configured to process one of thesecond sub-sampled image(s). The first trained model and the one or moresecond trained models may be of a same type or different types.

As described above, the first sub-sampled image may be generated usingthe first MRI sequence and the first sub-sampling rate, and each secondsub-sampled image may be generated using a second MRI sequence and asecond sub-sampling rate. In some embodiments, the first trained modelused to process the first sub-sampled image may correspond to the firstMRI sequence and the first sub-sampling rate. Additionally oralternatively, a second trained model used to process a specific secondsub-sampled image may correspond to the second MRI sequence and thesecond sub-sampling rate of the specific second sub-sampled image. Asused herein, if a trained model is trained based on a plurality ofsample sub-sampled images generated using a specific MRI sequence and aspecific sub-sampling rate, the trained model may be deemed as beingcorresponding to the specific MRI sequence and the specific sub-samplingrate. Merely by way of example, a trained model corresponding to a T1W1MRI sequence and a sub-sampling rate of 2 may be trained using aplurality of sample sub-sampled images generated using the T1WI MRIsequence with the sub-sampling rate of 2, and the trained model may beapplied to process a sub-sampled image acquired using the T1WI MRIsequence with the sub-sampling rate of 2. In some embodiments, the countof the trained models included of the image reconstruction model may bethe same as that of the MRI sequences or the sub-sampling ratescorresponding to the sub-sampled images.

In some embodiments, the first trained model may correspond to an MRIsequence other than the first MRI sequence and/or a sub-sampling rateother than the first sub-sampling rate. Additionally or alternatively, asecond trained model used to process a specific second sub-sampled imagemay correspond to an MRI sequence other than the second MRI sequenceand/or a sub-sampling rate other than the second sub-sampling rate.

In some embodiments, the first trained model and the one or more secondtrained models may be arranged according to their respectivesub-sampling rates in ascending order and sequentially connected.Optionally, during the application of the image reconstruction model, aninput of the first trained model may include an output of each of theone or more second trained models. For example, a first trained model M1may correspond to a first sub-sampling rate of 3, a second trained modelM2 may correspond to a second sub-sampling rate of 2, and a secondtrained model M3 may correspond to a second sub-sampling rate of 2.25.The first trained model M1, the second trained model M2, and the secondtrained model M3 may be arranged in the order of M2-M3-M1. An input ofthe second trained model M3 may include an output of the second trainedmodel M2. An input of the first trained model M1 may include the outputof the second trained model M2 and an output of the second trained modelM3. More descriptions regarding the image reconstruction model may befound elsewhere in the present disclosure. See, e.g., FIG. 7 andrelevant descriptions thereof. In some embodiments, a model including aplurality of sequentially connected sub-models may also be referred toas a cascade model.

In 530, the processing device 120B (e.g., the image generation module440) may generate a first full image of the subject corresponding to thefirst MRI sequence.

The first full image may be a predicted full image corresponding to thefirst MRI sequence reconstructed using the image reconstruction model.In the reconstruction process of the first full image, the imagereconstruction model may recover image features of the first sub-sampledimage that are lost during the corresponding sub-sampling scan. Thefirst full image of the subject may be close to an image of the subjectacquired by performing a full-sampling scan based on the first MRIsequence. In some embodiments, the processing device 120B may generatethe first full image of the subject based on the first sub-sampledimage, the second sub-sampled image(s), and the reference full image(s)using the image reconstruction model.

In some embodiments, operation 530 may be performed by process 600 asshown in FIG. 6.

In 610, for each of the second sub-sampled image(s), the processingdevice 1206 (e.g., the image generation module 440) may generate asecond full image by processing the second sub-sampled image using itscorresponding second trained model.

For a second sub-sampled image generated using a second MRI sequence,its second full image may be a predicted full image corresponding to thesecond MRI sequence reconstructed using the corresponding second trainedmodel. In the reconstruction process of the second full image, thecorresponding second trained model may recover image features of thesecond sub-sampled image that are lost during the correspondingsub-sampling scan. The second full image of the subject may be close toan image of the subject acquired by performing a full-sampling scanbased on the corresponding second MRI sequence.

In some embodiments, the second sub-sampled image(s) may include aplurality of second sub-sampled images corresponding to a plurality ofsecond sub-sampling rates. The processing device 120B may generate aranking result by ranking the plurality of second sub-sampled imagesaccording to their respective second sub-sampling rates in, for example,ascending order. The processing device 120B may then sequentiallyprocess the plurality of second sub-sampled images in the ranking resultto generate a plurality of second full images. For example, the secondsub-sampled images in the ranking result may be sequentially processedby their respective corresponding second trained models.

In 620, the processing device 120B (e.g., the image generation module440) may generate the first full image by processing the second fullimage(s) and the first sub-sampled image using the first trained model.

For example, the processing device 120B may input the second fullimage(s) and the first sub-sampled image into the first trained model,and the first trained model may output the first full image. In someembodiments, the reference full image(s) as described in connection withoperation 510 may also be inputted into the first trained model.

For illustration purposes, FIG. 7 provides an exemplary schematicdiagram showing a process for generating a first full image F1 accordingto some embodiments of the present disclosure. As shown in FIG. 7, thefirst full image F1 may be generated based on a first sub-sampled image11, a second sub-sampled image 12, a second sub-sampled image 13, and areference full image R using an image reconstruction model 700. Thefirst sub-sampled image 11 may be generated using a first MRI sequenceQ1 and a first sub-sampling rate of 3. The second sub-sampled image 12may be generated using a second MRI sequence Q2 a and a secondsub-sampling rate of 2. The second sub-sampled image 13 may be generatedusing a second MRI sequence Q2 b and a second sub-sampling rate of 2.25.The reference full image R may be generated using a third MRI sequenceQR.

The image reconstruction model may include a first trained model M1, asecond trained model M2, and a second trained model M3. The firsttrained model M1 may be configured to process the first sub-sampledimage 11, the second trained model M2 may be configured to process thesecond sub-sampled image 12, and the second trained model M3 may beconfigured to process the second sub-sampled image 13. In someembodiments, the first trained model M1 may correspond to the first MRIsequence Q1 and the first sub-sampling rate of 3; the second trainedmodel M2 may correspond to the second MRI sequence Q2 a and the secondsub-sampling rate of 2; and the second trained model M3 may correspondto the second MRI sequence Q2 b and the second sub-sampling rate of2.25. In the image reconstruction model 700, the first trained model M1,the second trained model M2, and the second trained model M3 may beranked according to their respective sub-sampling rates in ascendingorder (i.e., in an order of M2-M3-M1) and sequentially connected.

To generate the first full image F1, the processing device 120B mayfirst generate a second full image F2 corresponding to the secondsub-sampled image 12 and a second full image F3 corresponding to thesecond sub-sampled image 13. For example, the second sub-sampled image12 and the second sub-sampled image 13 may be ranked according to theirrespective second sub-sampling rates in ascending order. That is, thesecond sub-sampled image 12 may be ranked before the second sub-sampledimage 13 and processed first.

As illustrated in FIG. 7, the processing device 120B may input thesecond sub-sampled image 12 and the reference full image R into thesecond trained model M2. The second trained model M2 may process itsinput and generate the second full image F2. The output of the secondtrained model M2 (i.e., the second full image F2) may serve as a portionof an input of the second trained model M3. The processing device 1206may then input the second sub-sampled image 13, the second full imageF2, and the reference full image R into the second trained model M3. Thesecond trained model M3 may process its input and generate the secondfull image F3. The output of the second trained model M2 (i.e., thesecond full image F2) and the output of the second trained model M3(i.e., the second full image F3) may serve as a portion of an input ofthe first trained model M1. The processing device 120B may further inputthe first sub-sampled image 11, the second full image F2, the secondfull image F3, and the reference full image R into the first trainedmodel M1. The first trained model M1 may process its input and generatethe first full image F1.

It should be noted that the example illustrated in FIG. 7 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.

In some embodiments, the count of the trained models of the imagereconstruction model 700 may be modified. Merely by way of example, theimage reconstruction model 700 may include one or more additionaltrained models. For example, the image reconstruction model 700 mayinclude a second trained model M4 corresponding to a lower sub-samplingrate than the second trained model M2. As another example, the imagereconstruction model 700 may further include a third trained modelcorresponding to a higher sub-sampling rate than the first sub-samplingrate. In some embodiments, the reference full image R may be omitted.Alternatively, more than one reference full images may be used in thegeneration of the first full image F1. In some embodiments, the input ofthe first trained model M1 may be without the output of the secondtrained mode M2 (i.e., the second full image F2 may not be inputted intothe first trained model M1).

According to some embodiments of the present disclosure, the firstsub-sampled image corresponding to the first sub-sampling rate may bereconstructed into the first full image based on one or more secondsub-sampled images corresponding to the second sub-sampling rate(s)lower than the first sub-sampling rate and the reference full image(s)(optionally) of the subject using an image reconstruction model. Asdescribed elsewhere in this disclosure, the second sub-sampled image(s)may have more image features and physiological information than thefirst sub-sampled image corresponding to a higher sub-sampling rate.Therefore, the second sub-sampled image(s) and the reference full imagemay provide reference information for assisting the image reconstructionmodel to generate the first full image. The reference informationprovided by the reference full image and the second sub-sampled image(s)may facilitate the information recovery in the reconstruction of thefirst full image and improve the image quality of the first full image.

In this way, the total scan time of the subject may be reduced byadopting the sub-sampling imaging technique to acquire the first andsecond sub-sampled images instead of directly acquiring the first andsecond full images. Moreover, the reconstruction efficiency of the firstfull image may be improved because the application of the imagereconstruction model may obviate the need of performing a calibrationprocess between the first and second sub-sampled images.

In some embodiments, if the second sub-sampled image(s) include aplurality of sub-sampled images, they may be ranked according to theirrespective second sub-sampling rates, and a second sub-sampled imagecorresponding to a lower second sub-sampling rate may be reconstructedinto a second full image first. More reference information can beobtained if more second full images corresponding to the secondsub-sampled images are generated. Based on the reference information,the image reconstruction model may be able to generate a first fullimage with an improved accuracy even if the first sub-sampled image isacquired using a high first sub-sampling rate (e.g., a firstsub-sampling rate higher than a threshold). In this way, the total scantime in the MRI scanning process may be further reduced withoutcompromising the imaging quality.

It should be noted that the above description regarding FIGS. 5-7 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be addedinto a process or omitted from the process. Additionally oralternatively, two or more operations may be combined into a singleoperation, and/or an operation may be divided into two or moresub-operations. For example, operation 510 and operation 520 may becombined into a single operation. As another example, one or more otheroptional operations (e.g., a storing operation for storing a processingresult or an intermediate processing result) may be added in the process500.

FIG. 8 is a flowchart illustrating an exemplary process for generatingan image reconstruction model according to some embodiments of thepresent disclosure. In some embodiments, process 800 may be executed bythe MRI system 100. For example, the process 800 may be implemented as aset of instructions (e.g., an application) stored in a storage device(e.g., the storage device 130, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 120A (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4A) mayexecute the set of instructions and may accordingly be directed toperform the process 800.

In some embodiments, one or more operations of the process 800 may beperformed to achieve at least part of operation 520 as described inconnection with FIG. 5. In some embodiments, the process 800 may beperformed by another device or system other than the MRI system 100,e.g., a device or system of a vendor of a manufacturer. For illustrationpurposes, the following descriptions are described with reference to theimplementation of the process 800 by the processing device 120A, and notintended to limit the scope of the present disclosure.

In 810, the processing device 120A (e.g., the obtaining module 410) mayobtain a plurality of training samples each of which includes a firstsample sub-sampled image and one or more second sample sub-sampledimages of a sample subject.

As used herein, a sample subject may include a biological subject and/ora non-biological subject, such as a patient or a specific portion (e.g.,an organ or a tissue) of the patient. The sample subject and the subjectas described in connection with 510 may be of the same type or differenttypes. Two subjects may be deemed as being of the same type if theycorrespond to the same organ or tissue. In some embodiments, the samplesubjects of different training samples may be of the same type ordifferent types. For example, the sample subject of each training samplemay be the heart of a patient.

A sample sub-sampled image of a sample subject refers to a sub-sampledimage of the sample subject used as training data. The first samplesub-sampled image of a training sample may be generated based on thefirst MRI sequence and the first sub-sampling rate. Each second samplesub-sampled image of the training sample may be generated based on oneof the second MRI sequence(s) and one of the second sub-samplingrate(s).

In some embodiments, the training sample may include a first image pairand one or more second image pairs. The first image pair may include afirst ground truth full image and the first sample sub-sampled image ofthe sample subject of the training sample. The first ground truth fullimage may be generated by performing a full-sampling scan on the samplesubject according to the first MRI sequence. The first samplesub-sampled image may be generated based on the first ground truth fullimage and the first sub-sampling rate. For example, a set of sub-sampledk-space data may be determined by performing a sub-sampling operation onfull-sampled k-space data corresponding to the first ground truth fullimage, and the first sample sub-sampled image may be reconstructed fromthe set of sub-sampled k-space data. Each second image pair may includea second ground truth full image and a second sample sub-sampled imageof the sample subject. For a second image pair, the second ground truthfull image may be generated based on one of the one or more second MRIsequences, and the second sample sub-sampled image may be generatedbased on the second ground truth full image and one of the one or moresecond sub-sampling rates.

In some embodiments, each of the plurality of training samples mayfurther include one or more sample reference full images of thecorresponding sample subject. Each of the sample reference full image(s)may be generated based on one of the third MRI sequence(s). A samplereference full image of a sample subject may be similar to a referencefull image of the subject as described in connection with operation 510.

Merely by way of example, as shown in FIG. 9, a training sample of asample subject may include a first sample sub-sampled image 11′, a firstground truth full image G1, a second sample sub-sampled image 12′, asecond ground truth full image G2, a second sample sub-sampled image13′, a second ground truth full image G3, and a sample reference fullimage R′. The first ground truth full image G1 may be generated byperforming a full-sampling scan on the sample subject according to thefirst MRI sequence Q1. The first sample sub-sampled image 11′ may begenerated based on the first ground truth full image G1 and the firstsub-sampling rate of 3. The second ground truth full image G2 may begenerated by performing a full-sampling scan on the sample subjectaccording to the second MRI sequence Q2. The second sample sub-sampledimage 12′ may be generated based on the second ground truth full imageG2 and the second sub-sampling rate of 2. The second ground truth fullimage G3 may be generated by performing a full-sampling scan on thesample subject according to the second MRI sequence Q3. The secondsample sub-sampled image 13′ may be generated based on the second groundtruth full image G3 and the second sub-sampling rate of 2.25. The samplereference full image R′ may be generated by performing a full-samplingscan on the sample subject according to a third MRI sequence QR.

In some embodiments, a training sample of a sample subject may bepreviously generated and stored in a storage device (e.g., the storagedevice 130, the storage device 220, and/or the storage 390). Theprocessing device 120A may retrieve the training sample directly fromthe storage device. In some embodiments, at least a portion of thetraining sample may be generated by the processing device 120A. Forexample, the processing device 120A may obtain MRI signals of the samplesubject detected during a full-sampling scan of the sample subject fromthe MRI scanner 110, wherein the full-sampling scan may be performedaccording to the first MRI sequence. The processing device 120A mayfurther generate the first sample sub-sampled image and the first groundtruth full image of the sample subject based on the MRI signals of thesample subject.

In 820, the processing device 120A (e.g., the obtaining module 410) mayobtain a preliminary model including a first model and one or moresecond models.

The preliminary model refers to an algorithm or a model (e.g., a machinelearning model) to be trained as the image reconstruction model. Thefirst model refers to an algorithm or a model to be trained as the firsttrained model as described in connection with FIG. 5. The secondmodel(s) refers to an algorithm or a model to be trained as the secondtrained model(s) as described in connection with FIG. 5.

For the convenience of descriptions, the first model and the secondmodel(s) are referred to as a sub-model of the preliminary model. Insome embodiments, a sub-model may include a machine learning model, suchas a neural network model. For example, the sub-model may include apreliminary Fully Convolutional Network (FCN) model, a preliminary V-netmodel, a preliminary U-net model, a preliminary Alex network (AlexNet)model, a preliminary ResUNet model, a preliminary Visual Geometry Groupnetwork (VGGNet) model, or the like, or any combination thereof. In someembodiments, the preliminary model may be a preliminary cascade model inwhich the first model and the second model(s) are sequentiallyconnected. In some embodiments, the count of the sub-models included inthe preliminary model may be the same as that of the types of the firstand second MRI sequences. Additionally or alternatively, the count ofthe sub-models included in the preliminary model may be the same as thatof the types of the first and second sub-sampling rates.

In some embodiments, a sub-model of the preliminary model may includeone or more model parameters. Exemplary model parameters of thesub-model may include the number (or count) of layers, the number (orcount) of nodes, a loss function, or the like, or any combinationthereof. Before training, the sub-model may have one or more initialparameter values of the model parameter(s). In the training of thesub-model, the value(s) of the model parameter(s) of the sub-model maybe updated.

In some embodiments, the first model may be trained by the first samplesub-sampled images (or the first image pairs) of the training samples,which are generated based on the first MRI sequence and the firstsub-sampling rate. Accordingly, the first trained model trained from thefirst model may correspond to the first MRI sequence and the firstsub-sampling rate. The second sub-sampled images (or the second imagepairs) generated by a same second MRI sequence and a same sub-samplingrate may form a training set corresponding to the second MRI sequenceand the sub-sampling rate, which may be used to train one of the secondmodel(s) to generate a second trained model corresponding to the secondMRI sequence and the second sub-sampling rate. In some embodiments, atraining set used to train a sub-model may include images acquired bydifferent MRI sequences or different sub-sampling rates. For example,the first sample sub-sampled images (or the first image pairs) of thetraining samples may be acquired using the first MRI sequence anddifferent sub-sampling rates, or the first sub-sampling rate anddifferent MRI sequences. Accordingly, the first trained model trainedfrom the first model may only correspond to one of the first MRIsequence and the first sub-sampling rate.

In some embodiments, the first model and the second model(s) may besequentially trained according to their respective sub-sampling rates inascending order. For example, a first model M1′ may correspond to asub-sampling rate of 3, a second model M2′ may correspond to asub-sampling rate of 2, and a second model M3′ may correspond to asub-sampling rate of 2.25. The first model M1′, the second model M2′,and the second model M3′ may be trained in an order of M2′-M3′-M1′. Insome embodiments, the training of a specific sub-model of thepreliminary model may be performed based on one or more trainedsub-models generated before the training of the specific sub-model. Forexample, the first model M1′ may be trained based on the second trainedmodels trained from the second models M2′ and M3′. In some embodiments,the first model and the second model(s) may be trained jointly. Forillustration purposes, the following descriptions are described withreference to a training process in which the first model and the secondmodel(s) are trained sequentially, and not intended to limit the scopeof the present disclosure.

In 830, the processing device 120A (e.g., the model generation module420) may generate the one or more second trained models by training theone or more second models based on the one or more second samplesub-sampled images of each of the plurality of training samples.

In some embodiments, the one or more second models may be trained basedon the one or more second sub-sampled images of each of the plurality oftraining samples using a machine learning algorithm as describedelsewhere in this disclosure (e.g., FIG. 5 and the relevantdescriptions). As aforementioned, a second model may be trained using atraining set corresponding to a specific second MRI sequence and aspecific second sub-sampling rate. In some embodiments, the second modelmay be trained further based on the one or more sample reference fullimages of the training sample. For example, referring to FIG. 9 again,the second model M2′ may be trained first to generate the second trainedmodel M2 using the second sample sub-sampled image 12′, the secondground truth full image G2, and the sample reference full image R′ ofeach training sample.

In some embodiments, the training of the second model M2′ may include aniterative process including one or more iterations. For example, in acurrent iteration, an updated second model M2 generated in a previousiteration may be evaluated. For example, for each training sample, theprocessing device 120A may input the second sample sub-sampled image 12′and the sample reference full image R′ of the training sample into theupdated second model M2′, and the updated second model M2′ may generatean intermediate predicted full image corresponding to the second MRIsequence Q2. The processing device 120A may then determine a value of afirst loss function by comparing the intermediate predicted full imagewith the second ground truth full image G2 corresponding to the secondMRI sequence Q2 of each training sample.

The first loss function may be used to evaluate the accuracy andreliability of the updated second model M2′, for example, if the updatedsecond model M2′ is not overfitting, the smaller the first loss functionis, the more reliable the updated second model M2′ is. Exemplary firstloss functions may include an L1 loss function, a focal loss function, alog loss function, a cross-entropy loss function, a Dice loss function,etc. The processing device 120A may further update the value(s) of themodel parameter(s) of the updated second model M2′ to be used in a nextiteration based on the value of the first loss function according to,for example, a backpropagation algorithm. In some embodiments, the oneor more iterations may be terminated if a termination condition issatisfied in the current iteration. An exemplary termination conditionmay be that the value of the first loss function obtained in the currentiteration is less than a predetermined threshold. Other exemplarytermination conditions may include that a certain count of iterations isperformed, that the first loss function converges such that thedifferences of the values of the first loss function obtained inconsecutive iterations are within a threshold, etc. If the terminationcondition is satisfied in the current iteration, the processing device120A may designate the updated second model M2′ as the second trainedmodel M2.

After the second trained model M2 is generated, the second model M3′ maythen be trained based on the second trained model M2. For example, foreach training sample, the processing device 120A may input the secondsample sub-sampled image 12′ and the sample reference full image R′ intothe second trained model M2 to obtain a predicted image F2′ (or referredto as a predicted full image F2′) corresponding to the second MRIsequence Q2. The processing device 120A may then train the second modelM3′ using the predicted full image F2′, the second sample sub-sampledimage 13′, the second ground truth full image G3, and the samplereference full image R′ of each training sample. The training of thesecond model M3′ may be performed in a similar manner as that of thesecond model M2′. For example, in an iteration for training the secondmodel M3′, an updated second model M3′ may be evaluated. The processingdevice 120A may input the second sample sub-sampled image 13′, thesample reference full image R′, and the predicted image F2′ into theupdated second model M3′, and the updated second model M3′ may generatean intermediate predicted full image corresponding to the second MRIsequence Q3. The processing device 120A may then determine a value of asecond loss function by comparing the intermediate predicted full imagewith the second ground truth full image G3 corresponding to the secondMRI sequence Q3 of each training sample. The updated second model M3′may be evaluated based on the value of the second loss function in asimilar manner as how the updated second model M2′ is evaluated.

In 840, the processing device 120A (e.g., the model generation module420) may generate the first trained model by training the first modelbased on the first sample sub-sampled image of each of the plurality offirst training samples and the one or more second trained models.

For example, referring again to FIG. 9, the first model M1′ may betrained based on the second trained model M2 and the second trainedmodel M3. For example, for each training sample, the processing device120A may input the predicted image F2′, the second sample sub-sampledimage 13′, and the sample reference full image R′ into the secondtrained model M3 to obtain a predicted image F3′ corresponding to thesecond MRI sequence Q3. The processing device 120A may then train thefirst model M1′ using the predicted full images F2′ and F3′, the firstsample sub-sampled image 11′, the first ground truth full image G1, andthe sample reference full image R′ of each training sample.

The training of the first model M1′ may be performed in a similar manneras that of the second model M2′ as described in operation 830. Forexample, in an iteration for training the first model M1′, an updatedfirst model M1′ may be evaluated. The processing device 120A may inputthe predicted full images F2′ and F3′, the first sample sub-sampledimage 11′, and the sample reference full image R′ into the updated firstmodel M1′, and the updated first model M1′ may generate an intermediatepredicted full image corresponding to the first MRI sequence Q1. Theprocessing device 120A may then determine a value of a third lossfunction by comparing the intermediate predicted full image with thefirst ground truth full image G1 corresponding to the first MRI sequenceQ1 of each training sample. The updated first model M1′ may be evaluatedbased on the value of the third loss function in a similar manner as howthe updated second model M2′ is evaluated.

According to some embodiments of the present disclosure, in a trainingprocess, one or more predicted images corresponding to second MRIsequence(s) and one or more sample reference full images may be used inthe training of the first model. The predicted image(s) and the samplereference full image(s) may provide reference information that canfacilitate the training of the first model. For example, the referenceinformation provided by the predicted image(s) and the sample referencefull image may assist the first model to learn complex relationships(e.g., mapping relationships) between sample sub-sampled imagescorresponding to different sub-sampling rates, and learn an optimalmechanism to reconstruct an accurate first full image from acorresponding first sub-sampled image.

It should be noted that the above description regarding the process 800is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, the image reconstruction model may be stored in astorage device (e.g., the storage device 130) disclosed elsewhere in thepresent disclosure for further use. As another example, after the imagereconstruction model is generated, the processing device 120A mayfurther test the image reconstruction model using a set of testingsamples. As a further example, the processing device 120A may update theimage reconstruction model periodically or irregularly based on one ormore newly-generated training samples (e.g., new first samplesub-sampled images and new second sample sub-sampled images generated inmedical diagnosis, etc.).

In some embodiments, the preliminary model may include more sub-modelsother than the first model M1′, the second model M2′, and the secondmodel M3′. For example, the preliminary model may also include a secondmodel M4′ corresponding to a lower sub-sampling rate than the secondmodel M2′. As another example, the preliminary model may further includea third model corresponding to a higher sub-sampling rate than the firstsub-sampling rate. In some embodiments, two or more sample referencefull images may be used to train the preliminary model. In someembodiments, different sub-models may be trained using a same set ordifferent sets of training samples.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

We claim:
 1. A method for magnetic resonance imaging (MRI)reconstruction, comprising: obtaining a plurality of sub-sampled imagesof a subject, wherein the plurality of sub-sampled images include afirst sub-sampled image of the subject and one or more secondsub-sampled images of the subject, the first sub-sampled image isgenerated using a first MRI sequence and a first sub-sampling rate, eachof the one or more second sub-sampled images is generated using a secondMRI sequence and a second sub-sampling rate, and the second sub-samplingrate is smaller than the first sub-sampling rate; obtaining an imagereconstruction model having been trained according to a machine learningtechnique; and generating a first full image of the subjectcorresponding to the first MRI sequence based on the first sub-sampledimage, the one or more second sub-sampled images, and the imagereconstruction model.
 2. The method of claim 1, wherein the generating afirst full image of the subject corresponding to the first MRI sequencebased on the first sub-sampled image, the one or more second sub-sampledimages, and the image reconstruction model comprises: obtaining one ormore reference full images of the subject each of which is generatedusing one of one or more third MRI sequences; and generating the firstfull image of the subject corresponding to the first MRI sequence basedon the first sub-sampled image, the one or more second sub-sampledimages, the image reconstruction model, and the one or more referencefull image.
 3. The method of claim 1, wherein the image reconstructionmodel comprises: a first trained model configured to process the firstsub-sampled image, and one or more second trained models each of whichis configured to process one of the one or more second sub-sampledimages.
 4. The method of claim 3, wherein the first trained modelcorresponds to the first MRI sequence and the first sub-sampling rate,and each of the one or more second trained models corresponds to thesecond MRI sequence and the second sub-sampling rate of the secondsub-sampled image processed by the second trained model.
 5. The methodof claim 4, wherein in the image reconstruction model, the first trainedmodel and the one or more second trained models are arranged accordingto their respective sub-sampling rates in ascending order andsequentially connected.
 6. The method of claim 5, wherein an input ofthe first trained model includes an output of each of the one or moresecond trained models.
 7. The method of claim 3, wherein the generatinga first full image of the subject corresponding to the first MRIsequence based on the first sub-sampled image, the one or more secondsub-sampled images, and the image reconstruction model comprises: foreach of the one or more second sub-sampled images, generating a secondfull image by processing the second sub-sampled image using itscorresponding second trained model; and generating the first full imageby processing the one or more second full images and the firstsub-sampled image using the first trained model.
 8. The method of claim7, wherein the one or more second sub-sampled images include a pluralityof second sub-sampled images corresponding to a plurality of secondsub-sampling rates, and for each of the one or more second sub-sampledimages, the generating a second full image by processing the secondsub-sampled image using its corresponding second trained modelcomprises: generating a ranking result by ranking the plurality ofsecond sub-sampled images according to their respective secondsub-sampling rates in ascending order; and sequentially processing theplurality of second sub-sampled images in the ranking result to generatethe plurality of second full images.
 9. The method of claim 1, whereinthe image reconstruction model is generated according to a modeltraining process comprises: obtaining a plurality of training sampleseach of includes a first sample sub-sampled image and one or more secondsample sub-sampled images of the sample subject, the first samplesub-sampled image being generated based on the first MRI sequence andthe first sub-sampling rate, and each of the one or more second samplesub-sampled images being generated based on one of the one or moresecond MRI sequences and one of the one or more second sub-samplingrates; obtaining a preliminary model including a first model and one ormore second models; generating the one or more second trained models bytraining the one or more second models based on the one or more secondsample sub-sampled images of each of the plurality of training samples;and generating the first trained model by training the first model basedon the first sample sub-sampled image of each of the plurality oftraining samples and the one or more second trained models.
 10. Themethod of claim 9, wherein each of the plurality of training samplesfurther comprises one or more sample reference full images of thecorresponding sample subject, each of the one or more sample referencefull images being generated using one of the one or more third MRIsequences, and the one or more second trained models and the firsttrained model are generated further based on the one or more samplereference full images of each of the plurality of training samples. 11.The method of claim 9, wherein generating the first trained model bytraining the first model based on the first sub-sampled image of each ofthe plurality of training samples and the one or more second trainedmodels comprises: for each of the plurality of training samples,obtaining one or more predicted images corresponding to the one or moresecond MRI sequences based on the one or more second trained models andthe one or more second sample sub-sampled images of the training sample;and generating the first trained model by training the first model basedon the first sub-sampled image and the one or more predicted images ofeach of the plurality of training samples.
 12. A system for magneticresonance imaging (MRI) reconstruction, comprising: at least one storagedevice including a set of instructions; and at least one processorconfigured to communicate with the at least one storage device, whereinwhen executing the set of instructions, the at least one processor isconfigured to direct the system to perform operations including:obtaining a plurality of sub-sampled images of a subject, wherein theplurality of sub-sampled images include a first sub-sampled image of thesubject and one or more second sub-sampled images of the subject, thefirst sub-sampled image is generated using a first MRI sequence and afirst sub-sampling rate, each of the one or more second sub-sampledimages is generated using a second MRI sequence and a secondsub-sampling rate, and the second sub-sampling rate is smaller than thefirst sub-sampling rate; obtaining an image reconstruction model havingbeen trained according to a machine learning technique; and generating afirst full image of the subject corresponding to the first MRI sequencebased on the first sub-sampled image, the one or more second sub-sampledimages, and the image reconstruction model.
 13. The system of claim 12,wherein the generating a first full image of the subject correspondingto the first MRI sequence based on the first sub-sampled image, the oneor more second sub-sampled images, and the image reconstruction modelcomprises: obtaining one or more reference full images of the subjecteach of which is generated using one of one or more third MRI sequences;and generating the first full image of the subject corresponding to thefirst MRI sequence based on the first sub-sampled image, the one or moresecond sub-sampled images, the image reconstruction model, and the oneor more reference full image.
 14. The system of claim 12, wherein theimage reconstruction model comprises: a first trained model configuredto process the first sub-sampled image, and one or more second trainedmodels each of which is configured to process one of the one or moresecond sub-sampled images.
 15. The system of claim 14, wherein the firsttrained model corresponds to the first MRI sequence and the firstsub-sampling rate, and each of the one or more second trained modelscorresponds to the second MRI sequence and the second sub-sampling rateof the second sub-sampled image processed by the second trained model.16. The system of claim 15, wherein in the image reconstruction model,the first trained model and the one or more second trained models arearranged according to their respective sub-sampling rates in ascendingorder and sequentially connected.
 17. The system of claim 14, whereinthe generating a first full image of the subject corresponding to thefirst MRI sequence based on the first sub-sampled image, the one or moresecond sub-sampled images, and the image reconstruction model comprises:for each of the one or more second sub-sampled images, generating asecond full image by processing the second sub-sampled image using itscorresponding second trained model; and generating the first full imageby processing the one or more second full images and the firstsub-sampled image using the first trained model.
 18. The system of claim17, wherein the one or more second sub-sampled images include aplurality of second sub-sampled images corresponding to a plurality ofsecond sub-sampling rates, and for each of the one or more secondsub-sampled images, the generating a second full image by processing thesecond sub-sampled image using its corresponding second trained modelcomprises: generating a ranking result by ranking the plurality ofsecond sub-sampled images according to their respective secondsub-sampling rates in ascending order; and sequentially processing theplurality of second sub-sampled images in the ranking result to generatethe plurality of second full images.
 19. The system of claim 12, whereinthe image reconstruction model is generated according to a modeltraining process comprises: obtaining a plurality of training sampleseach of includes a first sample sub-sampled image and one or more secondsample sub-sampled images of the sample subject, the first samplesub-sampled image being generated based on the first MRI sequence andthe first sub-sampling rate, and each of the one or more second samplesub-sampled images being generated based on one of the one or moresecond MRI sequences and one of the one or more second sub-samplingrates; obtaining a preliminary model including a first model and one ormore second models; generating the one or more second trained models bytraining the one or more second models based on the one or more secondsample sub-sampled images of each of the plurality of training samples;and generating the first trained model by training the first model basedon the first sample sub-sampled image of each of the plurality oftraining samples and the one or more second trained models.
 20. Anon-transitory computer readable medium, comprising a set ofinstructions for magnetic resonance imaging (MRI) reconstruction,wherein when executed by at least one processor, the set of instructionsdirect the at least one processor to perform a method, the methodcomprising: obtaining a plurality of sub-sampled images of a subject,wherein the plurality of sub-sampled images include a first sub-sampledimage of the subject and one or more second sub-sampled images of thesubject, the first sub-sampled image is generated using a first MRIsequence and a first sub-sampling rate, each of the one or more secondsub-sampled images is generated using a second MRI sequence and a secondsub-sampling rate, and the second sub-sampling rate is smaller than thefirst sub-sampling rate; obtaining an image reconstruction model havingbeen trained according to a machine learning technique; and generating afirst full image of the subject corresponding to the first MRI sequencebased on the first sub-sampled image, the one or more second sub-sampledimages, and the image reconstruction model.